Learning Bayesian Networks Based on a Mutual Information Scoring Function and EMI Method

Authors:
Fengzhan Tian;Haisheng Li;Zhihai Wang;Jian Yu
Affiliations:
School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044, P.R. China;Baoding Training Center, Hebei Electric Power Corporation, Baoding 071000, P.R. China;School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044, P.R. China;School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

At present, most of the algorithms for learning Bayesian Networks (BNs) use EM algorithm to deal with incomplete data. They are of low efficiency because EM algorithm has to perform iterative process of probability reasoning to complete the incomplete data. In this paper we present an efficient BN learning algorithm, which use the combination of EMI method and a scoring function based on mutual information theory. The algorithm first uses EMI method to estimate, from incomplete data, probability distributions over local structures of BNs, then evaluates BN structures with the scoring function and searches for the best one. The detailed procedure of the algorithm is depicted in the paper. The experimental results on Asia and Alarm networks show that when achieving high accuracy, the algorithm is much more efficient than two EM based algorithms, SEM and EM-EA algorithms.